Cancer screening with early detection and intervention is a key factor for successful treatment and has shown potential to improve survival and reduce mortality of the cancer patients. ROC based methods have been extensively utilized to evaluate the performance of early detection. However, for evaluating early detection tests of cancer, ROC curves have been either underused or not always used in the best manner. Cancer researchers also need efficient methods to identify risk factors (biomarkers) that could improve the performance of early detection. Therefore, it is crucial to develop new methods for identifying and evaluating the prognostic and diagnostic risk factors (biomarkers) for earlier cancer detection and risk assessment. This pilot project focuses on developing novel ROC based computational models for risk factor identification, biomarker evaluation, and cancer prediction in cancer screening. Both linear and nonlinear models will be developed. It will provide efficient computational tools for earlier cancer detection, prevention, and risk assessment.